Overview

Dataset statistics

Number of variables16
Number of observations1836
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory217.2 KiB
Average record size in memory121.1 B

Variable types

DateTime1
Categorical4
Numeric11

Warnings

username has constant value "Benioff" Constant
cashtags has constant value "0" Constant
tweet has a high cardinality: 1836 distinct values High cardinality
mentions is highly correlated with number of tweetsHigh correlation
video is highly correlated with photos and 1 other fieldsHigh correlation
photos is highly correlated with videoHigh correlation
urls is highly correlated with number of tweetsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 2 other fieldsHigh correlation
mentions is highly correlated with number of tweetsHigh correlation
video is highly correlated with photos and 1 other fieldsHigh correlation
photos is highly correlated with videoHigh correlation
urls is highly correlated with number of tweetsHigh correlation
replies_count is highly correlated with retweets_count and 2 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 2 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 2 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 5 other fieldsHigh correlation
video is highly correlated with photosHigh correlation
photos is highly correlated with videoHigh correlation
urls is highly correlated with number of tweetsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with urlsHigh correlation
urls is highly correlated with mentions and 1 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
video is highly correlated with photos and 1 other fieldsHigh correlation
mentions is highly correlated with urls and 2 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
photos is highly correlated with video and 2 other fieldsHigh correlation
bins is highly correlated with percent changeHigh correlation
number of tweets is highly correlated with urls and 3 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
percent change is highly correlated with binsHigh correlation
cashtags is highly correlated with bins and 1 other fieldsHigh correlation
bins is highly correlated with cashtags and 1 other fieldsHigh correlation
username is highly correlated with cashtags and 1 other fieldsHigh correlation
tweet is uniformly distributed Uniform
date has unique values Unique
tweet has unique values Unique
mentions has 572 (31.2%) zeros Zeros
hashtags has 1582 (86.2%) zeros Zeros
video has 698 (38.0%) zeros Zeros
photos has 819 (44.6%) zeros Zeros
urls has 511 (27.8%) zeros Zeros
replies_count has 50 (2.7%) zeros Zeros
retweets_count has 28 (1.5%) zeros Zeros
percent change has 19 (1.0%) zeros Zeros

Reproduction

Analysis started2021-09-27 19:03:51.187263
Analysis finished2021-09-27 19:04:08.436977
Duration17.25 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Date

UNIQUE

Distinct1836
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Minimum2016-08-23 09:30:00
Maximum2021-05-28 09:30:00
2021-09-27T15:04:08.550596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:08.704395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1836
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O
 
1
Who would have thought we could raise $11M+ in one concert for @UCSFChildrens @UCSFBenioffOAK. $81M raising since 2010. Thank you Ohana!!! We love you so much!❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️ https://t.co/mn8grAeELb
 
1
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/IpXxC6frzw Dreamforce 2019 #DF19 https://t.co/faEVnJTxin Parker’s live behind the scenes tour of Dreamforce! https://t.co/smzDPnMUZK See the Free Mural @SFMOMA. “The mural, which @JRart calls a living painting, has more than 1200 people, incl local leaders @GavinNewsom, @CleveJones1, @Warriors @Money23Green, + members of the San Francisco Gay Men’s Chorus (@SFGMC)." -@hyperallergic https://t.co/bLym3gdqDF Calming down with the @thichnhathanh Monastics before Dreamforce. DF Attendees book a session with the Monastics on the Dreamforce App & learn how to meditate & breath. Excited to have the Monastics back for the 5th year! We even built a mini @plumvillageom this year!☮️ https://t.co/NaWeneorjX
 
1
Even if you aren’t at Dreamforce in person this year, you can watch and participate online. Tap “Set Reminder” below to get notified when the keynote starts and watch live! https://t.co/LpYULckps9 All of us need to give the Warriors a hand during these times. ❤️🏀 https://t.co/415erypha0
 
1
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/BgRYWLLqLh
 
1
Other values (1831)
1831 

Length

Max length4908
Median length293
Mean length450.672658
Min length11

Characters and Unicode

Total characters827435
Distinct characters304
Distinct categories20 ?
Distinct scripts5 ?
Distinct blocks18 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1836 ?
Unique (%)100.0%

Sample

1st rowAll docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O
2nd rowCongratulations @Salesforce 6 years as one of The World's Most Innovative Companies - Forbes - This year #2! https://t.co/YvyXpclp7B Incredible to be at @plumvillageom. Such a magical place. https://t.co/evqaDhPDHA Wonderful to be with Zen Master @thichnhathanh in @plumvillageom --incredible rebirth of health strength & energy. https://t.co/SPvrJjbdPU Thank you @alexrkonrad Welcome Salesforce Einstein---1st comprehensive AI platform for CRM. https://t.co/MrUk3m4SlV https://t.co/4qz6CXphOn All of my thoughts and prayers are with the people of Italy. May all the residents be protected and safe. Gd Bless. https://t.co/hefc0Fllng To all the friends of @thichnhathanh: I met with the venerable mindfulness teacher today and he was as strong & capable as I have seen him. Salesforce has issued its stakeholder report. We need feedback from all stakeholders on how to move forward. https://t.co/1MOiSl7Jml
3rd row"Would it be such a bad thing if doing business to change the world became the new normal?" @PledgeOne https://t.co/X2ZN72WdGU Thank you @greentechlady for writing in @FortuneMagazine on @PledgeOne and our 1-1-1 model. https://t.co/kgF8C6TU8R Wonderful to take three days of mindfulness with @thichnhathanh and the monastics at @plumvillageom. Coming home. https://t.co/LyiY521IxZ Thank you @greentechlady and @FortuneMagazine for introducing Salesforce Einstein. https://t.co/tr4V0Dd3fq Thank you @POTUS @McCauley_Lab @SenBrianSchatz @OceanElders for expanding #Papahanaumokuakea! https://t.co/EJAKhANQhq Mahalo Malama Aina!
4th rowThank you @Lilly_Ledbetter! https://t.co/expO7Ti9RQ #WomensEqualityDay Thank you @PattyArquette!
5th rowWake up call from @PMaurerICRC ceo @ICRC now 65M refugees/24 new refugees every minute. How can we help them? https://t.co/OQAgzJKJau Very excited to get to @Dreamforce and reveal our vision for Einstein---our trusted comprehensive AI platform. https://t.co/ndUhNN9xxx

Common Values

ValueCountFrequency (%)
All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O1
 
0.1%
Who would have thought we could raise $11M+ in one concert for @UCSFChildrens @UCSFBenioffOAK. $81M raising since 2010. Thank you Ohana!!! We love you so much!❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️ https://t.co/mn8grAeELb1
 
0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/IpXxC6frzw Dreamforce 2019 #DF19 https://t.co/faEVnJTxin Parker’s live behind the scenes tour of Dreamforce! https://t.co/smzDPnMUZK See the Free Mural @SFMOMA. “The mural, which @JRart calls a living painting, has more than 1200 people, incl local leaders @GavinNewsom, @CleveJones1, @Warriors @Money23Green, + members of the San Francisco Gay Men’s Chorus (@SFGMC)." -@hyperallergic https://t.co/bLym3gdqDF Calming down with the @thichnhathanh Monastics before Dreamforce. DF Attendees book a session with the Monastics on the Dreamforce App & learn how to meditate & breath. Excited to have the Monastics back for the 5th year! We even built a mini @plumvillageom this year!☮️ https://t.co/NaWeneorjX1
 
0.1%
Even if you aren’t at Dreamforce in person this year, you can watch and participate online. Tap “Set Reminder” below to get notified when the keynote starts and watch live! https://t.co/LpYULckps9 All of us need to give the Warriors a hand during these times. ❤️🏀 https://t.co/415erypha01
 
0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/BgRYWLLqLh1
 
0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/AM7s2viPxk Salesforce Tower Tonight. Photo Credit: L.Kessler https://t.co/6pq2vX2yJ11
 
0.1%
Riveting @maggierogers at the Time 100 Next! https://t.co/ab5a0uTdms Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/R05cUHc8dR1
 
0.1%
Thank you Andrew! ❤️ @andrewrsorkin https://t.co/00iaffMoZq1
 
0.1%
Dreamforce. 27 Keynote Soeakers. 2200 Sessions. 150,000 attendees. 10,000,000 online. 2M Trailblazers. Fleetwood Mac. https://t.co/R6OEd3osZ3 Once again @TeamTime crushes it with Time 100 Next. So proud of them! ⚡️ #TIME100Next: Spotlighting 100 rising stars who are shaping the future of their fields. https://t.co/QCZxuixm191
 
0.1%
I am with my #datafam at the one and only @tableau conference! Amazing energy. https://t.co/1Vb3RZIKE4 Join us! @awscloud, @Genesys, & Salesforce come together with @linuxfoundation to standardize interoperability across cloud apps. Thanks @btaylor for your leadership! All partners welcome to join the open source CIM. https://t.co/bgMXARPRs41
 
0.1%
Other values (1826)1826
99.5%

Length

2021-09-27T15:04:09.072260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the4608
 
3.9%
to3555
 
3.0%
of2496
 
2.1%
amp1979
 
1.7%
for1853
 
1.6%
a1776
 
1.5%
in1690
 
1.4%
and1580
 
1.3%
is1343
 
1.1%
you1276
 
1.1%
Other values (16693)96710
81.4%

Most occurring characters

ValueCountFrequency (%)
125395
15.2%
e63236
 
7.6%
t55133
 
6.7%
o50153
 
6.1%
a45824
 
5.5%
s39915
 
4.8%
i37568
 
4.5%
n36786
 
4.4%
r35987
 
4.3%
h26722
 
3.2%
Other values (294)310716
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter574769
69.5%
Space Separator125443
 
15.2%
Uppercase Letter54981
 
6.6%
Other Punctuation47830
 
5.8%
Decimal Number17057
 
2.1%
Final Punctuation1477
 
0.2%
Other Symbol1333
 
0.2%
Dash Punctuation1158
 
0.1%
Format916
 
0.1%
Currency Symbol572
 
0.1%
Other values (10)1899
 
0.2%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
490
36.8%
😷86
 
6.5%
🇸39
 
2.9%
🇺35
 
2.6%
🗽34
 
2.6%
👍31
 
2.3%
🌲29
 
2.2%
🇮25
 
1.9%
🇳24
 
1.8%
🇵18
 
1.4%
Other values (170)522
39.2%
Lowercase Letter
ValueCountFrequency (%)
e63236
 
11.0%
t55133
 
9.6%
o50153
 
8.7%
a45824
 
8.0%
s39915
 
6.9%
i37568
 
6.5%
n36786
 
6.4%
r35987
 
6.3%
h26722
 
4.6%
l24463
 
4.3%
Other values (20)158982
27.7%
Uppercase Letter
ValueCountFrequency (%)
T4475
 
8.1%
S4423
 
8.0%
C3652
 
6.6%
I2957
 
5.4%
A2843
 
5.2%
M2811
 
5.1%
W2439
 
4.4%
F2387
 
4.3%
O2374
 
4.3%
P2233
 
4.1%
Other values (17)24387
44.4%
Other Punctuation
ValueCountFrequency (%)
/14233
29.8%
.12380
25.9%
:5197
 
10.9%
@4944
 
10.3%
,2978
 
6.2%
!2207
 
4.6%
;2048
 
4.3%
&2025
 
4.2%
#536
 
1.1%
'465
 
1.0%
Other values (9)817
 
1.7%
Decimal Number
ValueCountFrequency (%)
03309
19.4%
12906
17.0%
22212
13.0%
51444
8.5%
41294
 
7.6%
31262
 
7.4%
91257
 
7.4%
71206
 
7.1%
81099
 
6.4%
61067
 
6.3%
Math Symbol
ValueCountFrequency (%)
+36
44.4%
=30
37.0%
|13
 
16.0%
~1
 
1.2%
1
 
1.2%
Format
ValueCountFrequency (%)
394
43.0%
359
39.2%
­146
 
15.9%
15
 
1.6%
2
 
0.2%
Space Separator
ValueCountFrequency (%)
125395
> 99.9%
 46
 
< 0.1%
1
 
< 0.1%
 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-865
74.7%
273
 
23.6%
20
 
1.7%
Currency Symbol
ValueCountFrequency (%)
$570
99.7%
£1
 
0.2%
1
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_335
96.8%
_11
 
3.2%
Final Punctuation
ValueCountFrequency (%)
1079
73.1%
398
 
26.9%
Close Punctuation
ValueCountFrequency (%)
)220
97.3%
]6
 
2.7%
Open Punctuation
ValueCountFrequency (%)
(177
96.7%
[6
 
3.3%
Initial Punctuation
ValueCountFrequency (%)
426
97.0%
13
 
3.0%
Modifier Symbol
ValueCountFrequency (%)
11
50.0%
🏽11
50.0%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Nonspacing Mark
ValueCountFrequency (%)
569
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ1
100.0%
Control
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin629750
76.1%
Common197099
 
23.8%
Inherited584
 
0.1%
Hangul1
 
< 0.1%
Hiragana1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
125395
63.6%
/14233
 
7.2%
.12380
 
6.3%
:5197
 
2.6%
@4944
 
2.5%
03309
 
1.7%
,2978
 
1.5%
12906
 
1.5%
22212
 
1.1%
!2207
 
1.1%
Other values (233)21338
 
10.8%
Latin
ValueCountFrequency (%)
e63236
 
10.0%
t55133
 
8.8%
o50153
 
8.0%
a45824
 
7.3%
s39915
 
6.3%
i37568
 
6.0%
n36786
 
5.8%
r35987
 
5.7%
h26722
 
4.2%
l24463
 
3.9%
Other values (47)213963
34.0%
Inherited
ValueCountFrequency (%)
569
97.4%
15
 
2.6%
Hangul
ValueCountFrequency (%)
1
100.0%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII822285
99.4%
Punctuation3007
 
0.4%
VS569
 
0.1%
Dingbats537
 
0.1%
None423
 
0.1%
Enclosed Alphanum Sup221
 
< 0.1%
Latin 1 Sup199
 
< 0.1%
Emoticons127
 
< 0.1%
Misc Symbols53
 
< 0.1%
Misc Technical4
 
< 0.1%
Other values (8)10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125395
15.2%
e63236
 
7.7%
t55133
 
6.7%
o50153
 
6.1%
a45824
 
5.6%
s39915
 
4.9%
i37568
 
4.6%
n36786
 
4.5%
r35987
 
4.4%
h26722
 
3.2%
Other values (80)305566
37.2%
Punctuation
ValueCountFrequency (%)
1079
35.9%
426
 
14.2%
398
 
13.2%
394
 
13.1%
359
 
11.9%
273
 
9.1%
20
 
0.7%
20
 
0.7%
15
 
0.5%
13
 
0.4%
Other values (5)10
 
0.3%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇸39
17.6%
🇺35
15.8%
🇮25
11.3%
🇳24
10.9%
🇵18
8.1%
🇯14
 
6.3%
🇬11
 
5.0%
🇫11
 
5.0%
🇷8
 
3.6%
🇻7
 
3.2%
Other values (10)29
13.1%
Dingbats
ValueCountFrequency (%)
490
91.2%
13
 
2.4%
9
 
1.7%
6
 
1.1%
5
 
0.9%
4
 
0.7%
3
 
0.6%
2
 
0.4%
2
 
0.4%
2
 
0.4%
VS
ValueCountFrequency (%)
569
100.0%
None
ValueCountFrequency (%)
🗽34
 
8.0%
👍31
 
7.3%
🌲29
 
6.9%
👽17
 
4.0%
🤘16
 
3.8%
11
 
2.6%
_11
 
2.6%
🏽11
 
2.6%
💻11
 
2.6%
👩11
 
2.6%
Other values (112)241
57.0%
Misc Symbols
ValueCountFrequency (%)
13
24.5%
8
15.1%
6
11.3%
4
 
7.5%
3
 
5.7%
3
 
5.7%
3
 
5.7%
3
 
5.7%
3
 
5.7%
2
 
3.8%
Other values (4)5
 
9.4%
Latin 1 Sup
ValueCountFrequency (%)
­146
73.4%
 46
 
23.1%
ç2
 
1.0%
Ì2
 
1.0%
á1
 
0.5%
£1
 
0.5%
°1
 
0.5%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Emoticons
ValueCountFrequency (%)
😷86
67.7%
🙏11
 
8.7%
😂10
 
7.9%
😇7
 
5.5%
😎6
 
4.7%
😱2
 
1.6%
🙀1
 
0.8%
😘1
 
0.8%
🙌1
 
0.8%
😜1
 
0.8%
Latin Ext A
ValueCountFrequency (%)
ā2
66.7%
ū1
33.3%
Modifier Letters
ValueCountFrequency (%)
ʻ1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Hiragana
ValueCountFrequency (%)
1
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Math Alphanum
ValueCountFrequency (%)
𝟬1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Benioff
1836 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters12852
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBenioff
2nd rowBenioff
3rd rowBenioff
4th rowBenioff
5th rowBenioff

Common Values

ValueCountFrequency (%)
Benioff1836
100.0%

Length

2021-09-27T15:04:09.327141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:09.401377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
benioff1836
100.0%

Most occurring characters

ValueCountFrequency (%)
f3672
28.6%
B1836
14.3%
e1836
14.3%
n1836
14.3%
i1836
14.3%
o1836
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11016
85.7%
Uppercase Letter1836
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f3672
33.3%
e1836
16.7%
n1836
16.7%
i1836
16.7%
o1836
16.7%
Uppercase Letter
ValueCountFrequency (%)
B1836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12852
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f3672
28.6%
B1836
14.3%
e1836
14.3%
n1836
14.3%
i1836
14.3%
o1836
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f3672
28.6%
B1836
14.3%
e1836
14.3%
n1836
14.3%
i1836
14.3%
o1836
14.3%

mentions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.089869281
Minimum0
Maximum36
Zeros572
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:10.198239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum36
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.793352519
Coefficient of variation (CV)1.336615904
Kurtosis22.63772032
Mean2.089869281
Median Absolute Deviation (MAD)1
Skewness3.470487506
Sum3837
Variance7.802818294
MonotonicityNot monotonic
2021-09-27T15:04:10.317386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0572
31.2%
1435
23.7%
2292
15.9%
3174
 
9.5%
4126
 
6.9%
582
 
4.5%
648
 
2.6%
727
 
1.5%
825
 
1.4%
916
 
0.9%
Other values (12)39
 
2.1%
ValueCountFrequency (%)
0572
31.2%
1435
23.7%
2292
15.9%
3174
 
9.5%
4126
 
6.9%
582
 
4.5%
648
 
2.6%
727
 
1.5%
825
 
1.4%
916
 
0.9%
ValueCountFrequency (%)
361
 
0.1%
271
 
0.1%
241
 
0.1%
192
 
0.1%
183
0.2%
172
 
0.1%
162
 
0.1%
154
0.2%
132
 
0.1%
126
0.3%

hashtags
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.197167756
Minimum0
Maximum7
Zeros1582
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:10.442790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5787448941
Coefficient of variation (CV)2.935291784
Kurtosis26.07480635
Mean0.197167756
Median Absolute Deviation (MAD)0
Skewness4.228184394
Sum362
Variance0.3349456525
MonotonicityNot monotonic
2021-09-27T15:04:10.552611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01582
86.2%
1178
 
9.7%
256
 
3.1%
314
 
0.8%
43
 
0.2%
51
 
0.1%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
01582
86.2%
1178
 
9.7%
256
 
3.1%
314
 
0.8%
43
 
0.2%
51
 
0.1%
61
 
0.1%
71
 
0.1%
ValueCountFrequency (%)
71
 
0.1%
61
 
0.1%
51
 
0.1%
43
 
0.2%
314
 
0.8%
256
 
3.1%
1178
 
9.7%
01582
86.2%

cashtags
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1836 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1836
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01836
100.0%

Length

2021-09-27T15:04:10.784237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:10.858649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
01836
100.0%

Most occurring characters

ValueCountFrequency (%)
01836
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1836
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01836
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01836
100.0%

video
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.088779956
Minimum0
Maximum9
Zeros698
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:10.933543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.271041725
Coefficient of variation (CV)1.167400003
Kurtosis4.865854915
Mean1.088779956
Median Absolute Deviation (MAD)1
Skewness1.844915635
Sum1999
Variance1.615547067
MonotonicityNot monotonic
2021-09-27T15:04:11.048118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0698
38.0%
1667
36.3%
2260
 
14.2%
3114
 
6.2%
452
 
2.8%
522
 
1.2%
615
 
0.8%
74
 
0.2%
92
 
0.1%
82
 
0.1%
ValueCountFrequency (%)
0698
38.0%
1667
36.3%
2260
 
14.2%
3114
 
6.2%
452
 
2.8%
522
 
1.2%
615
 
0.8%
74
 
0.2%
82
 
0.1%
92
 
0.1%
ValueCountFrequency (%)
92
 
0.1%
82
 
0.1%
74
 
0.2%
615
 
0.8%
522
 
1.2%
452
 
2.8%
3114
 
6.2%
2260
 
14.2%
1667
36.3%
0698
38.0%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.273965142
Minimum0
Maximum20
Zeros819
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:11.157698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.879937899
Coefficient of variation (CV)1.475658821
Kurtosis11.09279374
Mean1.273965142
Median Absolute Deviation (MAD)1
Skewness2.632929816
Sum2339
Variance3.534166503
MonotonicityNot monotonic
2021-09-27T15:04:11.275782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0819
44.6%
1539
29.4%
2171
 
9.3%
4103
 
5.6%
383
 
4.5%
553
 
2.9%
624
 
1.3%
818
 
1.0%
79
 
0.5%
99
 
0.5%
Other values (5)8
 
0.4%
ValueCountFrequency (%)
0819
44.6%
1539
29.4%
2171
 
9.3%
383
 
4.5%
4103
 
5.6%
553
 
2.9%
624
 
1.3%
79
 
0.5%
818
 
1.0%
99
 
0.5%
ValueCountFrequency (%)
201
 
0.1%
151
 
0.1%
131
 
0.1%
112
 
0.1%
103
 
0.2%
99
 
0.5%
818
 
1.0%
79
 
0.5%
624
1.3%
553
2.9%

urls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.468409586
Minimum0
Maximum16
Zeros511
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:11.396732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.615518704
Coefficient of variation (CV)1.100182619
Kurtosis8.760230291
Mean1.468409586
Median Absolute Deviation (MAD)1
Skewness2.272535728
Sum2696
Variance2.609900684
MonotonicityNot monotonic
2021-09-27T15:04:11.508476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1665
36.2%
0511
27.8%
2345
18.8%
3147
 
8.0%
472
 
3.9%
539
 
2.1%
626
 
1.4%
715
 
0.8%
85
 
0.3%
104
 
0.2%
Other values (3)7
 
0.4%
ValueCountFrequency (%)
0511
27.8%
1665
36.2%
2345
18.8%
3147
 
8.0%
472
 
3.9%
539
 
2.1%
626
 
1.4%
715
 
0.8%
85
 
0.3%
93
 
0.2%
ValueCountFrequency (%)
161
 
0.1%
113
 
0.2%
104
 
0.2%
93
 
0.2%
85
 
0.3%
715
 
0.8%
626
 
1.4%
539
 
2.1%
472
3.9%
3147
8.0%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct196
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.74618736
Minimum0
Maximum1125
Zeros50
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:11.655039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median15
Q336
95-th percentile132
Maximum1125
Range1125
Interquartile range (IQR)30

Descriptive statistics

Standard deviation63.80391335
Coefficient of variation (CV)1.836285308
Kurtosis69.59140423
Mean34.74618736
Median Absolute Deviation (MAD)12
Skewness6.321682872
Sum63794
Variance4070.939359
MonotonicityNot monotonic
2021-09-27T15:04:11.807291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292
 
5.0%
390
 
4.9%
176
 
4.1%
574
 
4.0%
670
 
3.8%
762
 
3.4%
462
 
3.4%
858
 
3.2%
1254
 
2.9%
952
 
2.8%
Other values (186)1146
62.4%
ValueCountFrequency (%)
050
2.7%
176
4.1%
292
5.0%
390
4.9%
462
3.4%
574
4.0%
670
3.8%
762
3.4%
858
3.2%
952
2.8%
ValueCountFrequency (%)
11251
0.1%
8111
0.1%
5951
0.1%
5031
0.1%
4671
0.1%
4361
0.1%
3971
0.1%
3851
0.1%
3771
0.1%
3671
0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct552
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.5174292
Minimum0
Maximum7239
Zeros28
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:11.961207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q137
median95
Q3230
95-th percentile754.25
Maximum7239
Range7239
Interquartile range (IQR)193

Descriptive statistics

Standard deviation388.355864
Coefficient of variation (CV)1.853573068
Kurtosis101.6679045
Mean209.5174292
Median Absolute Deviation (MAD)72
Skewness7.678635788
Sum384674
Variance150820.2771
MonotonicityNot monotonic
2021-09-27T15:04:12.114808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028
 
1.5%
1920
 
1.1%
2419
 
1.0%
1518
 
1.0%
1817
 
0.9%
4717
 
0.9%
2215
 
0.8%
2515
 
0.8%
1615
 
0.8%
4315
 
0.8%
Other values (542)1657
90.3%
ValueCountFrequency (%)
028
1.5%
110
 
0.5%
210
 
0.5%
39
 
0.5%
411
 
0.6%
511
 
0.6%
612
0.7%
712
0.7%
89
 
0.5%
911
 
0.6%
ValueCountFrequency (%)
72391
0.1%
63431
0.1%
30421
0.1%
30011
0.1%
29791
0.1%
26361
0.1%
25321
0.1%
25201
0.1%
24221
0.1%
23521
0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1088
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904.9455338
Minimum0
Maximum16564
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:12.275226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1163.75
median408.5
Q3976.25
95-th percentile3532.25
Maximum16564
Range16564
Interquartile range (IQR)812.5

Descriptive statistics

Standard deviation1529.980052
Coefficient of variation (CV)1.690687445
Kurtosis30.17048411
Mean904.9455338
Median Absolute Deviation (MAD)308.5
Skewness4.658805838
Sum1661480
Variance2340838.961
MonotonicityNot monotonic
2021-09-27T15:04:12.432756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
988
 
0.4%
1358
 
0.4%
1727
 
0.4%
667
 
0.4%
1117
 
0.4%
16
 
0.3%
2046
 
0.3%
3236
 
0.3%
7756
 
0.3%
156
 
0.3%
Other values (1078)1769
96.4%
ValueCountFrequency (%)
04
0.2%
16
0.3%
22
 
0.1%
33
0.2%
54
0.2%
65
0.3%
73
0.2%
82
 
0.1%
92
 
0.1%
103
0.2%
ValueCountFrequency (%)
165641
0.1%
159061
0.1%
150641
0.1%
139831
0.1%
126991
0.1%
116371
0.1%
115321
0.1%
113241
0.1%
112371
0.1%
112001
0.1%

number of tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.575708061
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:12.584397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum31
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.333090544
Coefficient of variation (CV)0.9058055063
Kurtosis22.42709342
Mean2.575708061
Median Absolute Deviation (MAD)1
Skewness3.495708836
Sum4729
Variance5.443311487
MonotonicityNot monotonic
2021-09-27T15:04:12.709931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1749
40.8%
2441
24.0%
3258
 
14.1%
4147
 
8.0%
580
 
4.4%
657
 
3.1%
736
 
2.0%
820
 
1.1%
1012
 
0.7%
910
 
0.5%
Other values (11)26
 
1.4%
ValueCountFrequency (%)
1749
40.8%
2441
24.0%
3258
 
14.1%
4147
 
8.0%
580
 
4.4%
657
 
3.1%
736
 
2.0%
820
 
1.1%
910
 
0.5%
1012
 
0.7%
ValueCountFrequency (%)
311
 
0.1%
221
 
0.1%
211
 
0.1%
191
 
0.1%
181
 
0.1%
162
 
0.1%
152
 
0.1%
141
 
0.1%
132
 
0.1%
125
0.3%

price
Real number (ℝ≥0)

Distinct1731
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.58811
Minimum66.58999634
Maximum283.4700012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.5 KiB
2021-09-27T15:04:12.856481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum66.58999634
5-th percentile73.73000145
Q1100.6999969
median146.5650024
Q3172.5175018
95-th percentile246.1331234
Maximum283.4700012
Range216.8800049
Interquartile range (IQR)71.81750488

Descriptive statistics

Standard deviation51.71778934
Coefficient of variation (CV)0.3552336062
Kurtosis-0.5424760603
Mean145.58811
Median Absolute Deviation (MAD)39.43250275
Skewness0.4728657302
Sum267299.77
Variance2674.729734
MonotonicityNot monotonic
2021-09-27T15:04:13.006913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.69999694
 
0.2%
154.57000733
 
0.2%
1623
 
0.2%
224.89999393
 
0.2%
83.559997563
 
0.2%
1603
 
0.2%
2703
 
0.2%
94.940002443
 
0.2%
158.75999452
 
0.1%
83.949996952
 
0.1%
Other values (1721)1807
98.4%
ValueCountFrequency (%)
66.589996341
0.1%
68.209999081
0.1%
68.2566631
0.1%
68.410003661
0.1%
68.419998171
0.1%
68.459999081
0.1%
69.150001531
0.1%
69.290000921
0.1%
69.325000761
0.1%
69.342500691
0.1%
ValueCountFrequency (%)
283.47000121
 
0.1%
276.32000731
 
0.1%
272.64999391
 
0.1%
272.13333131
 
0.1%
2711
 
0.1%
270.85333251
 
0.1%
270.57998661
 
0.1%
270.42666631
 
0.1%
2703
0.2%
267.63333131
 
0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1818
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0004603068847
Minimum-0.06618367407
Maximum0.1662115397
Zeros19
Zeros (%)1.0%
Negative842
Negative (%)45.9%
Memory size14.5 KiB
2021-09-27T15:04:13.153054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.06618367407
5-th percentile-0.02267109011
Q1-0.004871081758
median0.0006092832829
Q30.006504909175
95-th percentile0.02115178706
Maximum0.1662115397
Range0.2323952138
Interquartile range (IQR)0.01137599093

Descriptive statistics

Standard deviation0.01429250023
Coefficient of variation (CV)31.049938
Kurtosis14.01655829
Mean0.0004603068847
Median Absolute Deviation (MAD)0.005703514756
Skewness0.7037985043
Sum0.8451234403
Variance0.0002042755628
MonotonicityNot monotonic
2021-09-27T15:04:13.309242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
1.0%
0.0090740502211
 
0.1%
0.0019555568651
 
0.1%
0.005344935151
 
0.1%
-0.0074999739481
 
0.1%
0.0066495849411
 
0.1%
-0.0060399942131
 
0.1%
-0.0045783508261
 
0.1%
0.0036966326111
 
0.1%
0.004109157661
 
0.1%
Other values (1808)1808
98.5%
ValueCountFrequency (%)
-0.066183674071
0.1%
-0.065821627121
0.1%
-0.065755168741
0.1%
-0.064118617371
0.1%
-0.060856372521
0.1%
-0.059000015261
0.1%
-0.058300018311
0.1%
-0.056432779591
0.1%
-0.056192508061
0.1%
-0.055202050961
0.1%
ValueCountFrequency (%)
0.16621153971
0.1%
0.085776969841
0.1%
0.066299198181
0.1%
0.063815374421
0.1%
0.063162423461
0.1%
0.062296855441
0.1%
0.061510813321
0.1%
0.05929238491
0.1%
0.058252388761
0.1%
0.05400862481
0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
no change
1486 
rise
178 
drop
172 

Length

Max length9
Median length9
Mean length8.046840959
Min length4

Characters and Unicode

Total characters14774
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno change
2nd rowno change
3rd rowno change
4th rowno change
5th rowno change

Common Values

ValueCountFrequency (%)
no change1486
80.9%
rise178
 
9.7%
drop172
 
9.4%

Length

2021-09-27T15:04:13.571619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T15:04:13.651186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no1486
44.7%
change1486
44.7%
rise178
 
5.4%
drop172
 
5.2%

Most occurring characters

ValueCountFrequency (%)
n2972
20.1%
e1664
11.3%
o1658
11.2%
1486
10.1%
c1486
10.1%
h1486
10.1%
a1486
10.1%
g1486
10.1%
r350
 
2.4%
i178
 
1.2%
Other values (3)522
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13288
89.9%
Space Separator1486
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2972
22.4%
e1664
12.5%
o1658
12.5%
c1486
11.2%
h1486
11.2%
a1486
11.2%
g1486
11.2%
r350
 
2.6%
i178
 
1.3%
s178
 
1.3%
Other values (2)344
 
2.6%
Space Separator
ValueCountFrequency (%)
1486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13288
89.9%
Common1486
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2972
22.4%
e1664
12.5%
o1658
12.5%
c1486
11.2%
h1486
11.2%
a1486
11.2%
g1486
11.2%
r350
 
2.6%
i178
 
1.3%
s178
 
1.3%
Other values (2)344
 
2.6%
Common
ValueCountFrequency (%)
1486
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2972
20.1%
e1664
11.3%
o1658
11.2%
1486
10.1%
c1486
10.1%
h1486
10.1%
a1486
10.1%
g1486
10.1%
r350
 
2.4%
i178
 
1.2%
Other values (3)522
 
3.5%

Interactions

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2021-09-27T15:04:05.865219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:05.985881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.102406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.216977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.345131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.459280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.584174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.708071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.823687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:06.951981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.079282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.215507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.350764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.478732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.600611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-27T15:04:07.713735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-27T15:04:13.746301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T15:04:13.965193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T15:04:14.185568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T15:04:14.401082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T15:04:14.578476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T15:04:07.970738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T15:04:08.316315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
02016-08-23 09:30:00All docs, code, &amp; packing of the Atari games I wrote at 16 &amp;17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1OBenioff1001411234160177.8200000.000000no change
12016-08-24 09:30:00Congratulations @Salesforce 6 years as one of The World's Most Innovative Companies - Forbes - This year #2! https://t.co/YvyXpclp7B Incredible to be at @plumvillageom. Such a magical place. https://t.co/evqaDhPDHA Wonderful to be with Zen Master @thichnhathanh in @plumvillageom --incredible rebirth of health strength &amp; energy. https://t.co/SPvrJjbdPU Thank you @alexrkonrad Welcome Salesforce Einstein---1st comprehensive AI platform for CRM. https://t.co/MrUk3m4SlV https://t.co/4qz6CXphOn All of my thoughts and prayers are with the people of Italy. May all the residents be protected and safe. Gd Bless. https://t.co/hefc0Fllng To all the friends of @thichnhathanh: I met with the venerable mindfulness teacher today and he was as strong &amp; capable as I have seen him. Salesforce has issued its stakeholder report. We need feedback from all stakeholders on how to move forward. https://t.co/1MOiSl7JmlBenioff600473284641159778.000000-0.000256no change
22016-08-26 09:30:00"Would it be such a bad thing if doing business to change the world became the new normal?" @PledgeOne https://t.co/X2ZN72WdGU Thank you @greentechlady for writing in @FortuneMagazine on @PledgeOne and our 1-1-1 model. https://t.co/kgF8C6TU8R Wonderful to take three days of mindfulness with @thichnhathanh and the monastics at @plumvillageom. Coming home. https://t.co/LyiY521IxZ Thank you @greentechlady and @FortuneMagazine for introducing Salesforce Einstein. https://t.co/tr4V0Dd3fq Thank you @POTUS @McCauley_Lab @SenBrianSchatz @OceanElders for expanding #Papahanaumokuakea! https://t.co/EJAKhANQhq Mahalo Malama Aina!Benioff121011411102301580.3899990.002869no change
32016-08-26 16:00:00Thank you @Lilly_Ledbetter! https://t.co/expO7Ti9RQ #WomensEqualityDay Thank you @PattyArquette!Benioff21000193679180.029999-0.004478no change
42016-08-27 09:30:00Wake up call from @PMaurerICRC ceo @ICRC now 65M refugees/24 new refugees every minute. How can we help them? https://t.co/OQAgzJKJau Very excited to get to @Dreamforce and reveal our vision for Einstein---our trusted comprehensive AI platform. https://t.co/ndUhNN9xxxBenioff3001111277183280.3299990.003749no change
52016-08-28 09:30:00@s_scally https://t.co/owHDTlfoJH @codyrigsby what song was at the end of the ride this am it was a synthetic version of "remind me of someone I used to know" in last 5 mins Salesforce Einstein is Artitificial Intelligence for Everyone. Coming @Dreamforce. https://t.co/3vCLZdbdlXBenioff20022014255323380.2699990.003459no change
62016-08-30 09:30:00So sad Gene Wilder has passed on--one of my favorite actors of all time. Thank you @ConanOBrien for this interview. https://t.co/b6ky8uWnbsBenioff100001124131180.0000000.001001no change
72016-08-31 16:00:00Waiting @iamplusofficial @iamwill @bep new video #WHEREISTHELOVE tonight. Lets get Will to come back to @Dreamforce? https://t.co/8yyXyVM3mzBenioff4101100744179.419998-0.007374no change
82016-09-01 09:30:00Congratulations @iamwill! https://t.co/TTI0ecJe2F Thank you @jimcramer allowing us to announce our first $2B quarter! No other top 10 SW company is growing faster! https://t.co/12ySaosm3f Congratulations Salesforce on achieving your first $2B quarter! No other top 10 software company is growing faster! https://t.co/CaldjRMF4h This video is a glimpse into salesforce's Q2 FY17 results announced today and all of our journeys this quarter. https://t.co/VyeJL6dnt0 Congratulations Salesforce on achieving your first $2B quarter! No other top 10 software company is growing faster! https://t.co/ulIZtjs6OfBenioff200342256231162575.449997-0.049987drop
92016-09-01 16:00:00Great to be in Oakland with all the kids at Frick Middle School with @LibbySchaaf @mayoredlee @SFUSD_Supe. https://t.co/hbf7rVltur Thank you @LibbySchaaf for giving us the leadership to once again invest in the children of Oakland. https://t.co/DTeBoxGE9w The change in education that we all want starts with us and our own personal actions in our local public schools. https://t.co/UvXJfZCiWL Excited to expand our @SalesforceOrg public school program to include Oakland as well as SF. @OUSDNews @SFUnified https://t.co/0H7mrFVBVMBenioff70010315108247475.9100040.006097no change

Last rows

datetweetusernamementionshashtagscashtagsvideophotosurlsreplies_countretweets_countlikes_countnumber of tweetspricepercent changebins
18262021-05-20 16:00:00Congrats Jane! ❤️🌲Benioff0000003272471223.7799990.003588no change
18272021-05-21 09:30:00People expect corporations and CEOs to continue addressing the most pressing social and political issues even after the pandemic is over, according to new data. https://t.co/SwpZZKClVf Congrats Jane! Renowned conservationist Jane Goodall has been named as this year’s winner of the prestigious @TempletonPrize, honoring individuals whose life’s work embodies a fusion of science and spirituality. ❤️🌲 READ MORE: https://t.co/3qa0SWPxXY… https://t.co/t3wz9ZWAyoBenioff10011220374802225.2100070.006390no change
18282021-05-23 09:30:00Our 2nd relief aircraft on its way to India! @Salesforce and @vkhosla partnered with @GiveIndia to deliver this 2nd plane of much needed oxygen concentrators. Our hearts continue to be with our brothers &amp; sisters in India. May they all be protected, healed, &amp; blessed! ❤️🇮🇳🙏 https://t.co/eh6JPuz4x1 When former prime minister Indira Gandhi was asked about Bahuguna's movement, she said: "Well, frankly, I don't know all the aims of the movement. But if it is that trees should not be cut, I'm all for it." ❤️🌲 @1t_org https://t.co/xHuFA9k2xl Salesforce is already a Net Zero company. Now, Salesforce &amp; Accenture are showing our customers how to become Net Zero &amp; leaders using our Sustainability Cloud. Why? Because customers want it &amp; employees rally around it. Join us — put Sustainability first: https://t.co/9ZtoFQGWF7 https://t.co/Ubdhp422UMBenioff40024210052641263225.0700020.004553no change
18292021-05-24 09:30:00Where is the Love? Anti-Semitism. Anti-Palestinian. Anti-Asian. Anti-Muslim. Anti-Armenian. Anti-Black. Anti-LGBTQ. Anti-Christian. Anti-Latino. Anti-Jew. Anti-Arab. All are Anti-Love. How do we Love thy neighbor as thyself? May the One that brings Love bring Love to All. ❤️ https://t.co/qkl54ApayH Already weakened health systems across Yemen have been even further devastated by the impact of the COVID pandemic. 16.2M people in the country will face high levels of acute food shortages with an estimated 21,000 children at risk of falling into famine. https://t.co/GEbndOG6bNBenioff000111551498772225.000000-0.002306no change
18302021-05-25 09:30:00Our 2nd relief aircraft has landed in India! @Salesforce &amp; @vkhosla partnered with @GiveIndia to deliver a plane of much needed oxygen concentrators. Our hearts are with our brothers &amp; sisters in India. May they all be protected, healed, &amp; blessed! ❤️🇮🇳🙏 https://t.co/tuXX9Nm3TUBenioff300001221048431228.0000000.004450no change
18312021-05-25 16:00:00For me, meditation and leadership go hand-in-hand. On the newest episode of the "What I've Learned" podcast with @ariannahuff, I open up about how that connection is helping me shape a post-pandemic future: https://t.co/mH5NnkaZWR https://t.co/cnlwCYyG9T @harryhurst @SFDCTowerUKBenioff2001116382232227.710007-0.001272no change
18322021-05-26 09:30:00Welcome back London Ohana! ❤️🇬🇧 Last nights moon was so inspiring. From WSJ today: A ‘super blue blood moon’ in San Francisco, in 2018. A blood moon is when light filtered through Earth’s atmosphere during an eclipse turns the moon a rusty, reddish hue. PHOTO: JOSH EDELSON/AGENCE FRANCE-PRESSE/GETTY IMAGES https://t.co/HuHwwDrluoBenioff0001104484912228.4799960.003381no change
18332021-05-26 16:00:00Congrats @zackparisa @MaxNova90! “Mi­crosoft, which bought more than $3 mil­lion worth of the re­sult­ing carbon off­sets, helped @NCX map U.S. for­est data, such as tree species and wood vol­ume, us­ing satel­lite im­agery and com­put­ers.” https://t.co/m152NgplGXBenioff300001718971229.6799930.005252no change
18342021-05-27 16:00:00Our 2nd relief aircraft has landed in India &amp; contents delivered to @giveindia! @Salesforce &amp; @vkhosla landed the plane of much needed oxygen concentrators &amp; oximeters. Our hearts are with our brothers &amp; sisters in India. May they all be protected, healed, &amp; blessed! ❤️🇮🇳🙏 https://t.co/FqAoplDwz5Benioff300100341048661225.830002-0.016077drop
18352021-05-28 09:30:00Wonderful to be on @MadMoneyOnCNBC tonight announcing our record Q1 results. https://t.co/anwxjChHdB It’s happening! @Dreamforce ❤️ https://t.co/7VMP975nwv Here comes Dreamforce 2021! In person! All of our announcements today on @MadMoneyOnCNBC with @jimcramer tonight! 👍 https://t.co/anwxjChHdB Salesforce Growth: 2022 $26B (guidance) 2021 $21.25B 2020 $17.1B 2019 $13.2B 2018 $10.5B 2017 $8.4B 2016 $6.7B 2015 $5.4B 2014 $4.1B Thank you Ohana! ❤️ From 1st Earnings Call Inside. (We’re all vaccinated!) https://t.co/DQAwBNHm2ABenioff40011312840743974239.2200010.059292rise